%0 Dataset
%T "The Belt and Road" snow day data set (2000-2024)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/8a0f1cb0-bc9c-470b-9d73-49d92bdc9be1
%W NCDC
%R 10.12072/ncdc.nieer.db6899.2025
%A HAO Xiaohua 
%A ZHAO Qin
%A JI Wenzheng
%A GAO Weiqinag
%K The Belt and Road;snow coverage;snow coverage ratio
%X Aiming at the problem that the existing snow cover products in the "the Belt and Road" area are underestimated in mountain areas and woodlands, based on multi-source remote sensing data, a set of snow cover data in the "the Belt and Road" area was generated by using the MARS model combined with the method of land type characteristics to automatically identify snow cover. By leveraging the advantages of machine learning in solving nonlinear fitting problems, traditional snow remote sensing recognition can avoid misjudgment in complex terrain and terrain, and the accuracy of the product in mountainous and forested areas is significantly improved compared to existing MODIS snow products. On the basis of this product, a dataset of daily snow accumulation is calculated, which is defined as the corresponding date of the last 5 consecutive days of a hydrological year when snow accumulates.